隐变量耦合模型的相互依赖Hadamard核
A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings
摘要：
我们引入了一个新的内核，模型的输入依赖耦合多个潜在的过程。成对联合核以相互依赖的方式测量沿输入和跨不同潜在信号的协方差。潜在相关高斯过程（LCGP）模型通过依赖于输入的混合矩阵将这些非平稳潜在分量组合成多个输出。通过变分贝叶斯推断得到多观测集的概率分类和支持度。几个数据集上的结果表明，LCGP模型可以恢复潜在信号之间的相关性，同时实现最先进的性能。我们突出了潜在的协方差与EEG分类数据集，其中潜在的大脑过程及其耦合同时出现的模型。
英文摘要：
We introduce a novel kernel that models input-dependent couplings across multiple latent processes. The pairwise joint kernel measures covariance along inputs and across different latent signals in a mutually-dependent fashion. A latent correlation Gaussian process (LCGP) model combines these non-stationary latent components into multiple outputs by an input-dependent mixing matrix. Probit classification and support for multiple observation sets are derived by Variational Bayesian inference. Results on several datasets indicate that the LCGP model can recover the correlations between latent signals while simultaneously achieving state-of-the-art performance. We highlight the latent covariances with an EEG classification dataset where latent brain processes and their couplings simultaneously emerge from the model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
